Environmental Implications of Digital Addiction in Higher Education: A Scenario-Based Analysis of the Digital Carbon Footprint

Abstract

This study investigates the relationship between digital addiction and the carbon footprint of online learning using a scenario-based modeling approach. Three scenarios were designed to represent different levels of addiction: low, medium, and high. The calculation model was based on weekly parameters, including live class duration, replay viewing, and electricity grid emission factors. Energy coefficients were set at 0.20 kWh/h for live classes, 0.15 kWh/h for replay viewing, and 0.45 kg CO2e /kWh for the electricity grid. The model explicitly incorporates both content delivery–related energy consumption and device-level power usage (Pdevice). The findings indicate that higher levels of digital addiction are associated with greater carbon footprints per student. Across scenarios, semester emissions ranged from 3.8 kg CO2e in the low-addiction scenario to 7.0 kg CO2e in the high-addiction scenario (about 0.50 kg per week). Each additional hour of replay viewing produced approximately 0.07-0.08 kg CO2e of extra weekly emissions. In Scenario C, late-night usage was represented as additional replay time, serving as a behavioral proxy within the model. Overall, the results suggest that digital addiction not only affects academic and psychosocial well-being but also contributes to overlooked environmental costs. The study highlights the value of integrating learning analytics into sustainability-oriented educational practices and calls for further research using real LMS data and behavioral logs to validate the framework. Policy implications include moderating autoplay features, encouraging transcript-based learning alternatives, and increasing awareness of the digital carbon footprint among students and institutions.

Keywords

• Digital addiction
• digital carbon footprint
• online learning
• sustainability
• data analytics

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